



import torch

from transformers import AutoModel


class   __lowerCAmelCase  (		torch.nn.Module   ):
					"""simple docstring"""






					def __init__(  self       :							str					,       _snake_case       :							str="sayef/fsner-bert-base-uncased"       ):






								"""simple docstring"""


								super(UpperCAmelCase_					,       self       ).__init__()

								A__	  =     AutoModel.from_pretrained(UpperCAmelCase_					,       return_dict=UpperCAmelCase_       )
								A__	  =     torch.nn.CosineSimilarity(3					,       1E-08       )
								A__	  =     torch.nn.Softmax(dim=1       )



					def      _a				(  self       :							str					,       **_snake_case       :							int       ):






								"""simple docstring"""


								return self.bert(**UpperCAmelCase_       ).last_hidden_state



					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							Tuple       ):






								"""simple docstring"""


								return token_embeddings.sum(2					,       keepdim=UpperCAmelCase_       )



					def      _a				(  self       :							List[Any]					,       _snake_case       :							Optional[int]					,       _snake_case       :							Tuple					,       _snake_case       :							Optional[Any]=1       ):






								"""simple docstring"""


								return self.softmax(T * self.cos(UpperCAmelCase_					,       UpperCAmelCase_       )       )



					def      _a				(  self       :							Any					,       _snake_case       :							Optional[Any]					,       _snake_case       :							int       ):






								"""simple docstring"""


								A__	  =     W_supports['sizes'].tolist()
								A__	  =     W_supports['start_token_id'].item()
								A__	  =     W_supports['end_token_id'].item()

								del W_supports["sizes"]
								del W_supports["start_token_id"]
								del W_supports["end_token_id"]

								A__	  =     self.BERT(**UpperCAmelCase_       )
								A__	  =     self.BERT(**UpperCAmelCase_       )

								A__	  =     None
								A__	  =     None

								A__	  =     W_supports['input_ids'] == start_token_id
								A__	  =     W_supports['input_ids'] == end_token_id

								for i, size in enumerate(UpperCAmelCase_       ):
											if i == 0:
														A__	  =     0
											else:
														A__	  =     support_sizes[i - 1]

											A__	  =     S[s : s + size][start_token_masks[s : s + size]]
											A__	  =     S[s : s + size][end_token_masks[s : s + size]]

											A__	  =     torch.matmul(q[i]					,       s_start.T       ).sum(1       ).softmax(0       )
											A__	  =     torch.matmul(q[i]					,       s_end.T       ).sum(1       ).softmax(0       )

											if p_starts is not None:
														A__	  =     torch.vstack((p_starts, p_start)       )
														A__	  =     torch.vstack((p_ends, p_end)       )
											else:
														A__	  =     p_start
														A__	  =     p_end

								return p_starts, p_ends






"""simple docstring"""


def 						lowerCamelCase__      (				UpperCAmelCase_ = 60_08_51_47_51_43					)->				int:







       """simple docstring"""

       try:
              UpperCamelCase       			=		int(UpperCAmelCase_					)
       except (TypeError, ValueError):
              raise TypeError("Parameter n must be int or castable to int."					)
       if n <= 0:
              raise ValueError("Parameter n must be greater than or equal to one."					)
       UpperCamelCase       			=		2
       UpperCamelCase       			=		0
       if n == 2:
              return 2
       while n > 2:
              while n % i != 0:
                     i += 1
              UpperCamelCase       			=		i
              while n % i == 0:
                     UpperCamelCase       			=		n // i
              i += 1
       return int(UpperCAmelCase_					)


if __name__ == "__main__":
      print(F'''{solution() = }''')





